In this work, we propose a novel, fast, and memory-efficient unsupervised statistical method, combined with an unsupervised deep learning (DL) model, for de-snowing LiDAR point clouds in a fully unsupervised fashion. The results obtained on the real-scanned WADS dataset show that our model achieves a 6.3% improvement in mIOU over the current state-of-the-art unsupervised DL methods and performs comparable to supervised counterparts, substantially narrowing the performance gap between supervised and unsupervised approaches. Along with that, our model also outperforms its nearest competitor by 12.8% mIOU when tested on our CADC annotations. Additionally, our de-snowing algorithm enhances downstream semantic segmentation and object detection tasks without even requiring any modifications to the base segmentation and detection models. The source code, trained models, and online supplementary information are available at: https://sporsho.github.io/3DUnOutDet.

Adverse weather conditions such as snow, rain, and fog are natural phenomena that can impair the performance of the perception algorithms in autonomous vehicles. Although LiDARs provide accurate and reliable scans of the surroundings, its output can be substantially degraded by precipitation (e.g., snow particles) leading to an undesired effect on the downstream perception tasks. Several studies have been performed to battle this undesired effect by filtering out precipitation outliers, however, these works have large memory consumption and long execution times which are not desired for onboard applications. To that end, we introduce a novel outlier detector for 3D LiDAR point clouds captured under adverse weather conditions. Our proposed detector 3D-OutDet is based on a novel convolution operation that processes nearest neighbors only, allowing the model to capture the most relevant points. This reduces the number of layers, resulting in a model with a low memory footprint and fast execution time, while producing a competitive performance compared to state-of-the-art models. We conduct extensive experiments on three different datasets (WADS, SnowyKITTI, and SemanticSpray) and show that with a sacrifice of 0.16% mIOU performance, our model reduces the memory consumption by 99.92%, number of operations by 96.87%, and execution time by 82.84% per point cloud on the real-scanned WADS dataset. Our experimental evaluations also showed that the mIOU performance of the downstream semantic segmentation task on WADS can be improved up to 5.08% after applying our proposed outlier detector. We release our source code, supplementary material and videos in https://sporsho.github.io/3DOutDet . Upon clicking the link you will have to option to go to source code, see supplementary information and view videos generated with our 3D-OutDet.